Instituto Nacional de Estadística (INE)

Sub-Directorate General for Environmental, Agricultural and Financial Statistics

Av Manoteras 50-52
Planta 3ª modulo 0364

28050 Madrid 

The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force.  They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.

The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.

The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.

Data are arranged in tables using many classifications. Please find below information on most classifications.

The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2018/1874.

The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.

The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 3.

The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.

The list of core variables is set in Annex III of Regulation (EU) 2018/1091.

The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2020 are set in Commission Implementing Regulation (EU) 2018/1874.

The following groups of variables are collected in 2020:

• for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
• for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
• for the module "Rural development": support received by agricultural holdings through various rural development measures;
• for the module "Animal housing and manure management":  animal housing, nutrient use and manure on the farm, manure application techniques, facilities for manure.

See sub-category below.

The agricultural holding is a unit, both technical and economic, which has a single management and which carries out economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groupings:

- A.01.1: Non-perennial crops
- A.01.2: Perennial crops
- A.01.3: Plant propagation
- A.01.4: Animal production
- A.01.5: Mixed farming or
- The "maintenance of agricultural land in good agricultural and environmental condition" of group A.01.6 within the economic territory of the Union, either as a principal or secondary activity.

For the activities of class A.01.49, only the activities of "Raising of semi-domesticated or other live animals" (with the exception of insect farming) and "Beekeeping and production of honey and beeswax" are included.

The national definition of an agricultural holding is a unit, from a technical and economic point of view, with a single address, and which, within the Spanish economic territory, carries out agricultural activities, both as a main and secondary activity. In addition, the holding may have other complementary (non-agricultural) activities. This unit, being technically and economically unique, is characterised by a common use of labour and means of production (machinery, land, installations, fertilisers, etc.). This implies that, if the parcels of the holding are located in two or more municipalities, they may not be very far apart geographically.

The agricultural holding can therefore be defined as a unit of an agricultural nature (set of land and/or livestock), under a single management, located in a given geographical location, and using the same production methods.

See sub-categories below.

The thresholds of agricultural holdings are available in the annex.

The subset of population of agricultural holdings defined in item 3.6.1 which falls in the main frame i.e. above at least one of the thresholds set in Regulation (EU) 2018/1091.

The above answer holds for the modules ‘Labour force and other gainful activities’ and ‘Rural development’. The module ‘Machinery and equipment’ is not collected in 2020.

The subset of the population of agricultural holdings defined in item 3.6.2 with at least one of the following types of livestock: bovine animals, pigs, sheep, goats, poultry, between October 1, 2019 and 30 September 2020, not as at 30 September 2020.
The country conducted the module AHMM on a coverage larger than the minimum required by the Regulation.

See sub-categories below.

The entire territory of the country.

Canary Islands - Balearic Islands - Ceuta - Melilla

To geolocate the agricultural holdings, proceed as follows:
- Regarding the information collected by administrative registry, if the coordinates are valid, the GRID is created with them, otherwise the SIGPAC reference will be taken, provided that it is not duplicated with another holding; failing this, the cadastral reference will be taken, provided that it is not duplicated. If, after tracking these variables, no coordinates are obtained, they will be set to zero.
- For the information collected by questionnaire, the SIGPAC reference is looked at first, if it is valid, coordinates are taken and the GRID is generated; if it is not valid, the cadastral reference of the land is taken, if it is valid, coordinates are taken and the GRID is generated; if it is not valid, the cadastral reference of the livestock installations is taken, if it is valid, coordinates are taken and the GRID is generated. If these three references are not valid, an auxiliary file is looked at with all the holdings with geolocation data (obtained from different administrative sources). If coordinate data are obtained, the GRID is generated, and if not, the coordinates are set to zero.
For those holdings whose coordinates have been set to NULL, their geolocation will be imputed according to the holder's residence. For those farms that continue without georeferencing, the centroid of a grid of the municipality chosen probabilistically according to a weighting table that assigns greater weight to areas with a high density of farms will be established.

Farm structure statistics in our country cover the period from 1962 onwards. Older time series are described in the previous quality reports (national methodological reports).

The 2020 data are processed (by Eurostat) with 2017 standard output coefficients (calculated as a 5-year average of the period 2015-2019). For more information, you can consult the definition of the standard output.

Two kinds of units are generally used:

• the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
• the number of agricultural holdings having these characteristics.

The unit of measurement used for the area of the agricultural holding and crops is the hectare, except in the case of cultivated mushrooms where it is the square metre.

Livestock data can be expressed in number of heads of animals of the different types of livestock. Heads of livestock are given on a reference day within the reference period (30 September 2020), as the number of livestock in a year may fluctuate.

Livestock units (LSU) is a standard unit of measurement that allows the aggregation of various categories of livestock of different species and ages according to convention, through the use of specific coefficients established on the basis of nutritional or feed requirements of each type of animal, to allow a comparison. The coefficients used are adopted in accordance with Annex I of Regulation (EU) 2018/1091.

Farm labour data are expressed either in number of working days, in percentage of working time or in annual work units (AWU); one AWU is equivalent to the work done by one person on a full-time basis over one year. The annual work unit (AWU) is equivalent to the work done by a full-time person over a year, i.e. the total hours worked divided by the average annual hours worked in full-time jobs in the country.

For the module on animal housing and manure management, the relevant units are the average number of animals and the number of stalls.
- The average number of animals will be used for reporting greenhouse gas emissions, even for the simplest methodology is the average annual population, which for animals that are not static populations.
- The unit for assessing the size of animal housing is the number of places. The term places refers to the capacity of the stalls during the agricultural season. The number of temporarily empty stalls in the stables is also recorded, if these are normally occupied. For outdoor animals always, the number of stalls refers to the number of animals supported by the farm, assuming a reasonable stocking density per hectare.

For the application of manure on land, the percentage range of manure is considered.

The output of an agricultural characteristic is the monetary value of gross production at the farm gate price. Standard Production (SP) means the value of production corresponding to the average situation in a certain region for each agricultural characteristic.
Output means the sum of the value of the main product(s) and the secondary product(s). The values shall be calculated by multiplying the output per unit by the farm gate price excluding VAT (Value Added Tax), product taxes and direct payments.
Standard outputs shall be determined using average basic data calculated over a reference period of five years. They are updated from time to time in line with economic trends.
The total standard output of the holding is the sum of the values obtained for each characteristic by multiplying the standard outputs per unit by the corresponding number of units.
Holdings are classified according to their economic size into different classes. The economic size of the holding shall be defined in terms of the total standard output of the holding, expressed in euro.

For the variables included in "Other poultry (A5000X5100)", (A5210, A5220, A5230, A5410, A5240_5300) the disaggregated SOCs for each type of poultry are used, instead of using the SOC for other poultry.
Since our country has made the effort to create these coefficients, the 2017 SOCs of various disaggregated products were sent on a voluntary basis, they should be taken into account in the IFS. In this way, farms will be better classified by taking them into account and we will do so in our publications.

In relation to dairy cows (A23000F) and breeding female buffaloes (A2410), we have some farms where we have non-dairy buffaloes and dairy cows (which are not buffalo cows). The SO calculation is being done differently from Eurostat, as we have disaggregated information on breeding female buffaloes (dairy and non-dairy), and we have coefficients for both dairy and non-dairy cows.

See sub-categories below.

The use of land refers to the reference year 2020. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.

For the characteristics related to land the reference period is the 2020 agricultural campaign, from 1 October 2019 to 30 September 2020.

The 12-month reference period for the total irrigable area, which coincides with the cropping season, starting on 1 October 2019, and ending on 30 September 2020.

For variables on livestock the reference date shall be 30 September 2020. For variables on animal housing the reference period is the 2020 agricultural campaign, from 1 October 2019 to 30 September 2020.

For variables on manure management the reference period is the 2020 agricultural campaign, from 1 October 2019 to 30 September 2020. This period includes the reference day used for livestock.

For variables on labour force the reference period is the 2020 agricultural campaign, from 1 October 2019 to 30 September 2020.

The three-year period ending on 31 December 2020.

The reference day 30 September 2020 within the reference year 2020.

See sub-categories below.

The collection, processing and dissemination of data from statistical operations for state purposes is governed by the provisions of Law 12/1989, of 9 May, on the Public Statistical Function, and in the Fourth Additional Provision of Law 4/1990, of 29 June. The Public Statistical Function Act states that the National Statistical Plan is the main instrument for organising the statistical activity of the State Administration and contains the statistics to be compiled in the four-year period by the services of the State Administration or any other entities dependent on it.  All statistics included in the National Statistical Plan are statistics for state purposes and are compulsory.The National Statistical Plan 2017-2020, approved by Royal Decree 410/2016, of 31 October, includes the Agrarian Census 2020, which is also a statistic for state purposes.

https://www.boe.es/buscar/doc.php?id=BOE-A-1989-10767

https://www.boe.es/eli/es/rd/2016/10/31/410

For Law 12/1989 of 9 May 1989 on the Public Statistical Function (LFEP), the year of entry into force is 1989; and for the Royal Decree 410/2016, of 31 October, the year of entry into force is 2017.

Data exchanges between INE and the other statistical services of the State (ministerial departments, autonomous bodies and public entities of the State Administration), as well as between these and the statistical services of the Autonomous Communities for the development of the statistics entrusted to them, are regulated in the LFEP. The LFEP also establishes the mechanisms for statistical coordination between administrations, as well as the conclusion of cooperation agreements when deemed appropriate.
The data has been obtained in collaboration with the Basque Statistical Institute (EUSTAT) in the territorial scope of its community, in accordance with the agreement signed between the INE and EUSTAT.

The statistical data provided to the National Statistics Institute is protected by statistical secrecy. Statistical Secrecy is a guarantee and trust mechanism for respondents that implies the protection of the data that is obtained for statistical purposes.

Chapter III of the aforementioned Public Statistical Function (LFEP) regulates all aspects of statistical secrecy.

Therefore, the INE adopts the necessary logical, physical and administrative measures to ensure that the protection of confidential data is effective, from data collection to publication.

A legal clause is included in the information collection questionnaires informing about the protection of the data collected.

In the information processing phases, directly identifiable data are removed and additional measures are taken to ensure the security and integrity of the information. The LFEP obliges statistical services to "adopt the necessary organizational and technical measures to protect the information". Specifically, the security policy applied at INE follows the standards of the Spanish national security framework.

In the publication of the results tables, the detail of the information is analyzed in order to prevent confidential data from being deduced from the statistical units, applying direct and indirect anonymization techniques.

The Agrarian Census is a statistical operation included in the National Statistical Plan, so it is subject to the Law of the Public Statistical Function of May 9, 1989, so its data are protected by the Statistical Secrecy in all phases of its elaboration.

See sub-categories below.

See sub-categories below.

For tabular data published with a disaggregation level of NUTS 3, data swapping and global recoding techniques have been used.

For  tabular data with a higher level of disaggregation (at municipal level), techniques of global recoding and then suppression of primary and secondary cells, based mainly on the frequency rule, have been used. 

In particular, cells have been suppressed when the contribution is less or equal than 3 statistical units.

See sub-categories below.

The INE provides users with microdata information, aggregating the data in a way that preserves the direct or indirect identification of the statistical unit. The variable that is most often aggregated to preserve the statistical secrecy of the unit is the regional variable 

Also, we grant access to our microdata for scientific purposes only keeping the same security as proposed by the EU methodology.

There is a calendar of structural statistics in which the publication of the Agrarian Census is included.

The publication of the Agricultural Census data at national level will be on 29 April 2022, once the information from the Census has been sent to Eurostat.

https://www.ine.es/daco/daco41/calen.htm

The data are disseminated simultaneously according to the publication schedule to all interested parties, in most cases accompanied by a press release. most cases accompanied by a press release. At the same time, the data are published on the INE's website (www.ine.es). Tailor-made requests are also sent to registered users. Some users may receive information under embargo as specified in the European Statistics Code of Practice.

We don’t have a quality rating system. Relative Standard Errors (RSE) are calculated and released for then main crops and livestock characteristics. We advise that DATA have not quality if they have a RSE greater than 25%.

The Agricultural Census is disseminated every 10 years as established in the Regulation on which it is based. The latest Regulation (EU) 2018/1091 considers the realisation of an Agricultural Census in the year 2020 for the member states, with the exception of Portugal.

See sub-categories below.

https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176851&menu=ultiDatos&idp=1254735727106

The results of statistical operations are generally disseminated with press releases that can be consulted both in the menu corresponding to the operation and in the press releases section.

See sub-categories below.

Not applicable.

See sub-categories below.

The published information is available as of 4 May 2022. The number of accesses for data queries has been 45,492 in 2022, and 31,484 until July 2023.

https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176851&menu=resultados&idp=1254735727106

See sub-category below.

Not available.

Not requested.

See sub-categories below.

Not requested.

Not applicable.

Not applicable.

Censo Agrario 2020 - Metodología - Mayo 2022

https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176851&menu=metodologia&idp=1254735727106

The quality assurance framework for INE statistics is based on the ESSCoP, Eurostat's Code of Practice for European Statistics.
A series of measures have been implemented that contribute to guaranteeing the quality of the process and the results. Among them are the following:
- Both the questionnaire and the definitions are agreed in a working group with the participation of experts.
- Data collection through a multichannel media. We note CATI, CAWI applications with implementation of errors and warnings of incompatibility or incongruity between the survey responses in order to perform a first debugging.
- Editing and imputation after the collection of information.

See sub-categories below.

The quality of the process has been assured by the analysis, integration, standardisation and exhaustiveness (completeness) of all the agricultural files referred to in the Regulation, together with a fieldwork data collection to cover the entire target population of the agricultural census.
Since the beginning of the process, meetings have been held periodically with experts, where coverage has been studied and the coherence of the agricultural census data with other sources available in the Ministry of Agriculture has been analysed.

The availability of previous administrative information in the pre-census framework has enabled coverage control to be carried out, which has contributed to the achievement of a quality census.
The integration and standardisation of the administrative files to the concepts of the Regulation, together with the complementation of the direct collection, has made it possible to achieve 100% coverage.

Not available

The data are used by the public, researchers, farmers and policy makers to better understand the state of the agricultural sector and the impact of agriculture on the environment. The data track changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other EU policies.
The variables investigated, and their description, are reduced to the list proposed in Commission Implementing Regulation (EU) 2018/1874.

In this Census there are no variables collected only for national purposes.

In the preparatory work for the census, when the information collection questionnaire was being drawn up, our main users were consulted and a draft of the questionnaire was provided so that they could inform us of any shortcomings or missing variables.
They found the questionnaire to be correct and did not miss any information.

Not applicable.

A user satisfaction survey has been conducted in 2019.

The latest user satisfaction survey conducted by NSI is from 2019.

Information on low- and zero prevalence variables is available on Eurostat's website.

The IFS 2020 meets all the requirements set out in national and international regulations. Of the 299 characteristics to be provided to Eurostat, and due to the diversity of our agriculture, only two are considered non-significant (practically non-existent) and are therefore not investigated in the census that has been carried out in accordance with Regulation (EU) 2018/1091 of the European Parliament and of the Council and the Commission Implementing Regulation (EU) 2018/1874.

This makes the indicator Rate of available compulsory statistical results 99.33% and makes us the EU country with the highest number of characteristics investigated.

Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.

See categories below.

See sub-categories below.

For the modules collected by sample and for the sample from frame extension:

The relative sampling errors of the main crop and livestock characteristics are calculated and compliance with the precision requirements established in the Annex 5 of the Regulation is analyzed.

For the data collected by sample and census mode:

Editing and reweighting procedures are applied for the non-response cases. The high response rate and the procedures applied for the treatment of non-response lead to reduce the possible biases caused by it.

The non-response by negative or non-located is analyzed to known its eligibility. If this non-response corresponds to active holding in 2020 year its core data are imputed. Data modules are reweighted.

We comply with all precision requirements established in Annex 5 of the Regulation except for Labour force module and the variable ‘Pasture and meadow’ in NUTS 2 ES30. In this case, the RSE is the 5.1%. The NUTS 2 ES30 has less than 10,000 holdings but its NUTS 1 coincides with the NUTS 2.   
The Pasture and meadow variable represents 8.51% of the Utilised Agricultural Area in the region.  
The gross sample size for this NUTS 2 was calculated by optimum allocation in 1,412 holdings; the net sample size has been the 1,010 holdings.

To set the sample size that meets the precision requirements, the prevalence of the variable in the pre-census data of the nucleus is taken into account;
At the time of the sample design for the modules, the 2020 census is not available because census and module data are collected simultaneously.

The gross sample size to represent the frame extension was 1400 units and the net sample size has been only of 554 units; the main reason has been the lack of updated sample frame for this relevant population; the sample frame was mainly built using agricultural census 2009.

Concerning ES6 and Poultry, we designed the sample having in mind that the prevalence of the variable do not qualify this case for precision requirements. With data from pre-census (before to collect the 2020 census) that it was our sampling frame, Poultry was not relevant for this NUTS 1.

Concerning ES62 and Sheep and goats, we designed the sample having in mind that the prevalence of the variable do not qualify this case for precision requirements.

Concerning ES43 and Poultry, we designed the sample having in mind that the prevalence of the variable do not qualify this case for precision requirements.

Concerning ES70 and Fresh vegetables, strawberries, flowers and ornamental the gross sample size was 780 and the net sample size was 630. This reduction has led to an increase in sampling error.

The gross sample size to represent the frame extension was 1400 units and the net sample size has been only of 554 units; the main reason has been the lack of updated sampling frame for this relevant population; the sampling frame was mainly built using agricultural census 2009.

Formulas are provided in the attached file Methodology used to calculate relative standard errors

See sub-categories below.

See sub-categories below.

The over-coverage rate is available in the annex. The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat. 

The over-coverage rate is 12.20%

Types of holdings included in the frame but not belonging to the population

We note that almost more of 40% are due to cease their activity; another 40% by below thresholds and around 11% by temporally out of production.

Actions to minimise the over-coverage error

To update data using the most recent administrative register:

- Agricultural Register

- Tax Register

- Population Register

Not requested.

See sub-categories below.

We cannot calculate the under-coverage rate.

Update the administrative register joined to fieldwork for all holdings out of them. 

To impute the non-response of eligible units.

All holdings belonging to the population of the core have been included in the frame; but it’s possible that some birth in 2020 year that  is not in the 2020 agricultural register or pay taxes , has not been included; we think that these cases will be minimal.

We have realized periodical meetings with different experts to analyze the coverage, comparing census data with other agricultural sources.

Misclassification errors have been minimized by having up-to-date records.

Contact errors have been minimized by having up-to-date records.

See sub-categories below.

The main causes of measurement errors are due to self-completion without interviewer assistance.

We have improved the questionnaire and the collection method with the experience gained from the previous census and surveys.

To ensure data consistency and minimise errors, we used an application (IRIA) developed by INE that integrated all the data collection and editing phases. All questionnaires (postal mail, CAWI, CATI) were recorded with IRIA.

During the collection and recording phases of the mailed questionnaires, the data were checked, with a quality control of the recording and a control of the data supplied. In addition, CAWI and CATI have their own checks in IRIA.

IRIA detects errors in the internal consistency of the questionnaires (partial absence of data in a questionnaire, inconsistent data between different variables and control of the range and existence of quantitative variables). It also detects and lists controls for outliers, such as crops that are not common in certain regions.

Post-recording editing was carried out centrally by the Promoter Unit with the help of an external company. After this manual correction of errors and before obtaining the datasets with the final data, all questionnaires were subjected to Automatic Data Imputation processes.

Not available

See sub-categories below.

The unit non-response rate is in the annex of item 13.3.1.1. The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings.  The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and if applicable frame extension, for which core data are sent to Eurostat.

For core data the unit non-response is the 10.4%.

Actions to minimise or address unit non-response:

- Update register

- To load additional information: telephone number

- Several phone call to holding titular.

We study the non-response unit to detect if they are eligible and in these cases, we apply imputation methods.

The characteristics related to the manager of the holding (year of birth, sex, working days, year started working as manager, training) are non-response, but no individual data are available for this item.

For the variables related to the manager of the holding, imputations have been carried out, in cases where the data was absent or erroneous: Y_BIRTH_MAN, SEX_MAN, WH_MAN_AWU_PC, Y_FARM_MAN and TNG_MAN.

FA9, has been imputed on 10,246 holdings, where there were livestock and no livestock facilities area was reported. In total there are 215,838 holdings with this variable, so the imputation rate is 4.74%.

UAAT_IB, for 157 holdings with rice cultivation, C2000T, with no data in the variable UAAT_IB, the value of C2000T has been imputed. There are 317,930 with area of irrigation facilities, so the imputation rate is 0.05%.

We cannot to calculate this impact but we have minimized it by imputation method.

See sub-categories below.

Several edits have been used during fieldwork.

The Responsible Department, the Information Technology Unit and the Sampling Unit are authorised to make corrections. The tools used to carry out these corrections are SAS programming and a custom-designed application for the loading of all Census information, in which manual filtering and macro-purification has been carried out.

We cannot to calculate this impact.

We don’t have model assumption error.

See sub-categories below.

No interim results have been published. 

The time lag of the final results of the census is 18 months. The reference time used for the calculation of the time lag is 30 September 2020, as this is the last day of the agricultural campaign in Spain.

See sub-categories below.

See sub-categories below.

Not requested.

Data will be published on the INE website on 29 April 2022, one month after sending data to Eurostat.

See sub-categories below.

Not applicable, because there are no mirror flows in Integrated Farm Statistics.

See sub-categories below.

Agricultural holdings with a definition different from Regulation (EU) 2018/1091 are not collected. There are not differences between the national definition and the EU definition of the holding.

Not applicable.

See sub-categories below.

With Agricultural Census 2009 data and updated agricultural registers, we have calculated the percentage of the total utilized agricultural area and livestock of the holdings that meet the thresholds listed in Annex II the Regulation.

We prove that with these thresholds, the data required cover 98% of the total utilized agricultural area and 98% of the livestock units of Spain; indeed, these thresholds also meet these 98% thresholds in each NUTS 2 region except for the NUTS 2 region ES11. For this reason, exclusively for the NUTS 2 region ES11, we apply the frame extension.

There are only differences for the NUTS2 ES11; the thresholds for ES11 are similar to the 2009 census. These are:
- Threshold for utilized agricultural area: 1 ha.
- Livestock: 1 LSU

The thresholds set in Regulation (EU) 2018/1091 for the IFS 2020 do not comply with 98% of the utilised agricultural and livestock area for NUTS2 ES11.

See sub-categories below.

Data are collected, sent to Eurostat and published in accordance with the definitions and classification of variables according to Regulation (EU) 2018/1091, Commission Implementing Regulation (EU) 2018/1874 and the EU manual.

The information is available in the annex. 
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings.

The information is available in the annex of item 15.1.4.1.1. 

The information is available in the annex of item 15.1.4.1.1. 

The livestock coefficients set out in Regulation 2018/1091 are used.

There are no differences between the types of livestock included under the heading 'Other livestock n.e.c.' and the types of livestock included according to the EU manual.

Not applicable.

See sub-categories below.

Data are collected, sent to Eurostat and published in accordance with the reference periods/reference days set out in Regulation (EU) 2018/1091.

Not applicable.

Not applicable

In the process of data collection, common land was collected, but in the process of purification, all the pastures of these entities were distributed among the livestock farms in the area. The common land has been distributed among the livestock farms in the same location (municipality or province), following a methodology analogous to that indicated in Annex II of the IFS-Handbook 2020.

Not applicable.

See sub-categories below.

There are no deviations in the national standards and rules for the certification of organic products from Council Regulation (EC) No 834/2007.

Not applicable.

The same methods are used for all autonomous communities. Spanish legislation has been adopted from the European legislation.

See sub-categories below.

Since the first survey in 1987 when we joined the Community Programme, the methodology has basically not changed. The only changes, induced by changes in the Community methodology, refer to the definition of the work-year unit (AWU), the definition of the technical-economic orientations (TEA) and the definition of the livestock units (LSU).
Since the 1993 survey, the definition of AWU has been modified from 275 to 228 full days.
The technical-economic guidelines are modified from the 1999 Agricultural Census onwards, replacing the main TEAs 11 and 12 with TEAs 13 and 14. Subsequently, with the latest reform of the CAP, the new Commission Regulation (EC) No. 1242/2008 of 8 December 2008 is approved, which affects the 2009 Agricultural Census, and the 2013 and 2016 Surveys. In the 2020 Census, these TEAs are used.
The coefficients used in the calculation of the LSU are also modified as of the 1999 Agricultural Census. The last modification of these coefficients is made as of the 2009 Census with the introduction of a coefficient for ostriches that were not the object of research. The same coefficients are still used in the 2020 Census, they have not changed.
Both the Technical-economic orientations and the Livestock units are derived variables obtained from the questionnaire data.
In 2020 the following changes have occurred: the thresholds have changed compared to those set in 2016, the collection method has changed completely, administrative information has been used for approximately 70% of the holdings, to fill in the CORE variables of the census.
Therefore, it can be comparable with data from previous surveys, but taking into account these changes.
For all variables except AWU, the number of comparable items in a time series since their last break is 12 (from 1987 to 2020), while for AWU this indicator is 11.

See sub-categories below.

Regulation (EU) 2018/1091 newly considers agricultural holdings with only fur animals. However even if our country raises fur animals, holdings with only fur animals are not included in our data collection because they do not meet the thresholds. The thresholds for animals are expressed in livestock units (LSU) and fur animals are not associated LSU coefficients. We did not add thresholds related to fur animals; there is no reason for it (fur animals do not contribute towards 98% of the total LSU).

See sub-categories below.

The thresholds were changed to be in line with Regulation (EU) 2018/1091. 

The most relevant changes in the thresholds for the 2020 Census compared to those established for the 2016 survey, is the reduction of the threshold for permanent crops, specifically those referring to olives and vineyards.

See sub-categories below.

Not applicable

See sub-categories below.

Legal personality of the agricultural holding

In IFS, there is a new class (“shared ownership”) for the legal personality of the holding compared to FSS 2016, which trigger fluctuations of holdings in the classes of sole holder holdings and group holdings.

Other livestock n.e.c.

In FSS 2016, deer were included in this class, but in IFS they are classified separately.

Also in FSS 2016, there was a class for the collection of equidae. That has been dropped and equidae are included in IFS in "other livestock n.e.c."

Livestock units

In FSS 2016, turkeys, ducks, geese, ostriches and other poultry were considered each one in a separate class with a coefficient of 0.03 for all the classes except for ostriches (coefficient 0.035). In IFS 2020, the coefficients were adjusted accordingly, with turkeys remaining at 0.03, ostriches remaining at 0.35, ducks adjusted to 0.01, geese adjusted to 0.02 and other poultry fowls n.e.c. adjusted to 0.001.

Organic animals

While in FSS only fully compliant (certified converted) animals were included, in IFS both animals under conversion and fully converted are to be included.

See sub-categories below.

The reference period for the rural development measures was in FSS 2016 a period of 2 years (from 1 January 2015 to 31 December 2016) and is in IFS 2020 a period of 3 years (from 1 January 2018 to 31 December 2020).

See sub-categories below.

The 2016 survey collected data from common land, and counted the agricultural area used within these fictitious holdings. In the 2020 census, the common land area has been reallocated to neighbouring livestock holdings in your municipality or province.

The analysis of the time series 2016-2020 brought to these observations:

• For the 2020 census, information from the Livestock Register has been used for the livestock variables, which implies an improvement in the coverage of the information, the levels published by the Ministry of Agriculture. With the use of administrative registers, an improvement in the coverage of permanent crops has also been achieved, in vineyards, hence the high differences in the CV in the aggregate of Grapes for other wines. Regarding tropical fruit trees, growth is mainly concentrated in Andalucía, and Extremadura.
• The reduction in the FARM_SPOU and FARM_FAM values is due to the new value that is reported in the 2020 Census, FARM_HLD_SPOUFAM, shared ownership between the holder and his/her spouse or with a family member other than the spouse. Farms that in 2016 came in the values: the head is the holder's spouse (FARM_SPOU) or the head is a relative of the holder other than the spouse (FARM_FAM), now in the 2020 census, some of these have been recorded in FARM_HLD_SPOUFAM (value that was not collected in 2016).
• Decrease of the OGA not directly related to the holding : Due to the changes in the thresholds, the agricultural holdings in 2020 belong to holders who are preferentially engaged in agricultural activity, i.e. the holdings are more specialised in the agricultural sector. The evolution is monitored with the Central Business Register, from the 2009 agricultural census, from which the sample of the structure of agricultural holdings for 2016 and the 2020 agricultural census is taken, there is a -17.6% decline. In addition, the ageing of the population has also led to a decline in non-farm income-generating activities.

See sub-categories below.

Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.

Not applicable, because Integrated Farm Statistics have no relevance for national accounts.

See sub-categories below.

The results were continuously evaluated during editing.
During centralised editing, the application indicated which source the holding was included in the pre-census frame of holdings and which collection method was used. The data that the holding had in the pre-census frame was displayed. This allowed the editor to compare the information at the micro level.

In addition, a comparison was made at the provincial level between the information from the 2016 survey, the data on Annual Crop Areas and Annual Crop Productions according to Regulation (EC) 543/2009 and the data from the 2020 Census.
Differences have been found for grassland data, due to different classification and definition methodologies. For the rest of the crops the data were more or less consistent.

See sub-categories below.

The cross domain comparison between APRO and IFS datasets, showed cases where data compared come from different sources, with different regulations and specific methodologies, which do not always coincide:
- Different concepts as in the variable Kitchen gardens (K0000). For the IFS, kitchen gardens with an area larger than 0.05 hectares are considered as vegetable area.
- Different target population. The IFS population is determined by the thresholds set out in Annex III of Regulation 2018/1091.
- Different collection methods.
- Different observation and survey unit. in the IFS the UAA of the agricultural holding is collected.
- Different way of collecting livestock. In the IFS the stock is collected at a fixed date, for 2020 it was 30 September 2020, which explains the variations between the data.

In the agricultural census 2020 a great effort has been made to coordinate the different sources, which is reflected in the decrease in variations compared to other years.

The data are internally consistent. This is ensured by the application of a wide range of validation rules.

See sub-categories below.

The 2020 agricultural census has not been coordinated with any survey. The collection questionnaires have been sent to the Ministry of Agriculture, so that it is informed and checks the need to introduce new variables.

For the 2020 census, administrative information has been used for CORE data for about 70% of the agricultural holdings.

See sub-categories below.

The average duration for collecting the core variables from the farm is 10.3 minutes.

The average duration for collecting the Labour force and other gainful activities variables from the farm is 6.5 minutes.

Not relevant, the collection of the Rural Development variables has been carried out by administrative register.

The average duration for collecting the Animal housing and manure management variables from the farm is 4.7 minutes.

Only the final data of the Census is published, and it is not subject to revision.

If errors are detected and the data needs to be modified, an explanatory note would be added to the information in order to inform users that the data has been changed.

The data have been revised throughout the whole process.
On a weekly basis, all the data were reviewed to check the changes made during the week and to compare the provisional results obtained with the previous data (last FSS and previous census) and the data from the Ministry of Agriculture. For this purpose, the Census data were downloaded and tabulated for comparison.

Not requested.

See sub-categories below.

See sub-categories below.

We have used the administrative registers referred to in article 4(2) of the Regulation (EU) 2018/1091.

See sub-categories below.

Not applicable for 2019/2020.

Not applicable for 2019/2020.

Not applicable for 2019/2020.

See sub-categories below.

A stratified random design was used.
The strata are formed by crossing Region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area and livestock units.

None

Using a Proportional allocation and requirements of minimum for stratum.

See sub-categories below.

A stratified random design was used.
The strata are formed by crossing Region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area and livestock units.

We determine take-all stratum chosen the largest holdings in each NUTS 2.  We also applied the Rule of the deviation sigma (Julien y Mandala, 1990)

We determine of overall sample size as result of optimum allocation. The requirements precision of the Annex 5 of the Regulation are established to calculate the sample size. We also increase the result the optimum allocation by preventing the non-response.

See sub-categories below.

Not applicable

Not applicable

Not applicable

See sub-categories below.

A stratified randomised design was used.
The strata are formed by crossing region (NUTS 2), technical-economic orientation and size, measured by utilised agricultural area and livestock units.

We determine take-all stratum chosen the largest holdings in each NUTS 2. We also applied the Rule of the deviation sigma (Julien y Mandala, 1990)

We determine of overall sample size as result of optimum allocation. The requirements precision of the Annex 5 of the Regulation are established to calculate the sample size. We also increase the result the optimum allocation by preventing the non-response.

The software tool used for sample selection was SAS (tailor-made programmes).

See sub-categories below.

The information is available on Eurostat's website.

See the attached Excel file in the Annex.

The information on innovative approaches and the quality methods applied is available on Eurostat's website.

The agricultural census is conducted every 10 years.  The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.

See sub-categories below.

Please find the questionnaire in annex.

See sub-categories below.

The IRIA (Integration of Information Collection and Administration) software was the tool used during data validation.

Three types of validation can be distinguished:

• Validation during data collection: there were controls included in the IRIA application in each of the collection phases (CAWI, postal collection, CATI). The controls were presented to the interviewers during the interview itself or at the end of the interview, depending on the type of error. In the case of postal collection, where the questionnaire was recorded once it had arrived by post, the controls were detailed at the end of the interview and resolved by telephone calls. Subsequently, the survey inspectors had to accept or reject each of the enumerators' questionnaires, depending on the types of errors they contained and the comments included in them. At the next level, the survey inspector carried out an overall inspection of the information collected.
• Validation in Central Services: once the questionnaires were marked as clean in the collection phase, the Department Responsible carried out a validation of the information, guided mainly by the identification of the observations to be dealt with, depending on the coherence in the evolution of the estimated data, with regard to the results available from previous Surveys or from the census. Likewise, a follow-up was carried out of the incidences in the collection.
• Automatic imputation: a processes of automatic imputation of the information was carried out, using a specific programs.

The population for the LAFO module is slightly larger than the population for CORE, because the LAFO population is an estimated based on a sample, while the CORE population is based on census data.

We apply calibration techniques, using CALMAR macro SAS, in the cases there are correlations between core and module variables.

Thus, for the LAFO module, small holdings are calibrated by the labour of the head of the holding and generally, in each UAA  sizes, it’s calibrated by the number of census holdings, hectares of cultivated area and of pastures.
For the AHMM module, it is calibrated, in each UAA sizes, by the census holdings belonging to this population, and at NUTS 2 level, it is calibrated  by different types of LSU.

The imputation rate is 10.39%

Covered under Data compilation.

Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.

See sub-categories below.

AHMM - Animal Housing and Manure Management

AWU – Annual Working Units

CALMAR - Calibration Programme

CAP – Common Agricultural Policy

CATI – Computer Assisted Telephone Interview

CAWI – Computer Assisted Web Interview

CORE - General, crops and livestock variables of Annex III of regulation 2018/1091

EC - European Community

EU - European Union

ESSCoP - Eurostat's Code of Practice for European Statistics

EUSTAT – Basque Statistical Institute

FSS – Farm Structure Survey

IACS – Integrated Administration and Control System

IFS – Integrated Farm Statistics

INE - National Statistical Institute

IRIA - Integration of Information Collection and Administration

LAFO - Labour Force

LFEP - Public Statistical Function Law

LSU – Livestock units

NACE – Nomenclature of Economic Activities

NSI – National Statistic Institute

NUTS – Nomenclature of territorial units for statistics

RSE – Relative Standard Errors

SGM – Standard Gross Main

SIGPAC – Geographical Information System for Agricultural Parcels

SO – Standard Output

SP - Standard Production

TEA - Technical-Economic Orientations

TIN – Tax Identification Number

UAA – Utilised agricultural area

VAT - Value Added Tax

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